A deflection yoke for projection TV with reduced geometrical distortion

Abstract— We have succeeded in coping with raster‐distortion and deflection‐defocusing problems in CRT projection TV by utilizing a longer horizontal coil and uniquely shaped windings. The field distribution along the tube axis was also modified and optimized to achieve minimum raster distortion and deflection defocusing simultaneously. The position of the centering magnet was shifted towards the screen side to minimize the defocusing of the spot.     

A tribe competition-based genetic algorithm for feature selection in pattern classification

Feature selection has always been a critical step in pattern recognition, in which evolutionary algorithms, such as the genetic algorithm (GA), are most commonly used. However, the individual encoding scheme used in various GAs would either pose a bias on the solution or require a pre-specified number of features, and hence may lead to less accurate results. In this paper, a tribe competition-based genetic algorithm (TCbGA) is proposed for feature selection in pattern classification. The population of individuals is divided into multiple tribes, and the initialization and evolutionary operations are modified to ensure that the number of selected features in each tribe follows a Gaussian distribution. Thus each tribe focuses on exploring a specific part of the solution space. Meanwhile, tribe competition is introduced to the evolution process, which allows the winning tribes, which produce better individuals, to enlarge their sizes, i.e. having more individuals to search their parts of the solution space. This algorithm, therefore, avoids the bias on solutions and requirement of a pre-specified number of features. We have evaluated our algorithm against several state-of-the-art feature selection approaches on 20 benchmark datasets. Our results suggest that the proposed TCbGA algorithm can identify the optimal feature subset more effectively and produce more accurate pattern classification.     

Wavelet-based feature extraction using probabilistic finite state automata for pattern classification

Real-time data-driven pattern classification requires extraction of relevant features from the observed time series as low-dimensional and yet information-rich representations of the underlying dynamics. These low-dimensional features facilitate in situ decision-making in diverse applications, such as computer vision, structural health monitoring, and robotics. Wavelet transforms of time series have been widely used for feature extraction owing to their time-frequency localization properties. In this regard, this paper presents a symbolic dynamics-based method to model surface images, generated by wavelet coefficients in the scale-shift space. These symbolic dynamics-based models (e.g., probabilistic finite state automata (PFSA)) capture the relevant information, embedded in the sensor data, from the associated Perron-Frobenius operators (i.e., the state-transition probability matrices). The proposed method of pattern classification has been experimentally validated on laboratory apparatuses for two different applications: (i) early detection of evolving damage in polycrystalline alloy structures, and (ii) classification of mobile robots and their motion profiles.     

A cost-based fuzzy system for pattern classification with class importance

This paper proposes a cost-based fuzzy classification system for pattern classification problems with an order of class importance. The task here is to minimize the misclassification of patterns from an important class. It is assumed that the classification importance is given for each class, not for each pattern. Another assumption is that only the order of importance is given for given classes without any numerical measures of importance. We show the performance of the proposed cost-based fuzzy classification system for a real-world pattern classification problem. This work was presented in part at the 12th International Symposium on Artificial Life and Robotics, Oita, Japan, January 25–27, 2007     

Multi-class pattern classification using neural networks

Multi-class pattern classification has many applications including text document classification, speech recognition, object recognition, etc. Multi-class pattern classification using neural networks is not a trivial extension from two-class neural networks. This paper presents a comprehensive and competitive study in multi-class neural learning with focuses on issues including neural network architecture, encoding schemes, training methodology and training time complexity. Our study includes multi-class pattern classification using either a system of multiple neural networks or a single neural network, and modeling pattern classes using one-against-all, one-against-one, one-against-higher-order, and P-against-Q. We also discuss implementations of these approaches and analyze training time complexity associated with each approach. We evaluate six different neural network system architectures for multi-class pattern classification along the dimensions of imbalanced data, large number of pattern classes, large vs. small training data through experiments conducted on well-known benchmark data.     

A simple and fast multi-class piecewise linear pattern classifier

A simple and fast multi-class piecewise linear classifier is proposed and implemented. For a pair of classes, the piecewise linear boundary is a collection of segments of hyperplanes created as perpendicular bisectors of line segments linking centroids of the classes or parts of classes. For a multi-class problem, a binary partition tree is initially created which represents a hierarchical division of given pattern classes into groups, with each non-leaf node corresponding to some group. After that, a piecewise linear boundary is constructed for each non-leaf node of the partition tree as for a two-class problem. The resulting piecewise linear boundary is a set of boundaries corresponding to all non-leaf nodes of the tree. The basic data structures of algorithms of synthesis of a piecewise linear classifier and classification of unknown patterns are described. The proposed classifier is compared with a number of known pattern classifiers by benchmarking with the use of real-world data sets.     

A net-structure learning system for pattern description

This paper discusses a computer program that recognizes and describes two-dimensional patterns composed of subpatterns. The program also recognizes all patterns in a scene consisting of several patterns.

Patterns are stored in a learned hierarchical, net-structure memory. Weighted links between memory nodes represent subpattern/pattern relationships. Both short term and permanent memories are used.

Pattern recognition is accomplished with a serial heuristic search algorithm, which attempts to search memory and compute input properties efficiently. Without special processing, the program can be asked to look for all occurrences of a specified pattern in a scene.     

Higher order feature selection for text classification

In this paper. we present the MIFS-C variant of the mutual information feature-selection algorithms. We present an algorithm to find the optimal value of the redundancy parameter, which is a key parameter in the MIFS-type algorithms. Furthermore, we present an algorithm that speeds up the execution time of all the MIFS variants. Overall, the presented MIFS-C has comparable classification accuracy (in some cases even better) compared with other MIFS algorithms, while its running time is faster. We compared this feature selector with other feature selectors, and found that it performs better in most cases. The MIFS-C performed especially well for the breakeven and F-measure because the algorithm can be tuned to optimise these evaluation measures. Jan Bakus received the B.A.Sc. and M.A.Sc. degrees in electrical engineering from the University of Waterloo, Waterloo, ON, Canada, in 1996 and 1998, respectively, and Ph.D. degree in systems design engineering in 2005. He is currently working at Maplesoft, Waterloo, ON, Canada as an applications engineer, where he is responsible for the development of application specific toolboxes for the Maple scientific computing software. His research interests are in the area of feature selection for text classification, text classification, text clustering, and information retrieval. He is the recipient of the Carl Pollock Fellowship award from the University of Waterloo and the Datatel Scholars Foundation scholarship from Datatel. Mohamed S. Kamel holds a Ph.D. in computer science from the University of Toronto, Canada. He is at present Professor and Director of the Pattern Analysis and Machine Intelligence Laboratory in the Department of Electrical and Computing Engineering, University of Waterloo, Canada. Professor Kamel holds a Canada Research Chair in Cooperative Intelligent Systems. Dr. Kamel's research interests are in machine intelligence, neural networks and pattern recognition with applications in robotics and manufacturing. He has authored and coauthored over 200 papers in journals and conference proceedings, 2 patents and numerous technical and industrial project reports. Under his supervision, 53 Ph.D. and M.A.Sc. students have completed their degrees. Dr. Kamel is a member of ACM, AAAI, CIPS and APEO and has been named s Fellow of IEEE (2005). He is the editor-in-chief of the International Journal of Robotics and Automation, Associate Editor of the IEEE SMC, Part A, the International Journal of Image and Graphics, Pattern Recognition Letters and is a member of the editorial board of the Intelligent Automation and Soft Computing. He has served as a consultant to many Companies, including NCR, IBM, Nortel, VRP and CSA. He is a member of the board of directors and cofounder of Virtek Vision International in Waterloo.     

基于边缘模式和主导学习框架的相似纹理分类

边缘是进行相似纹理图像分类的有效特征之一,为了提高边缘检测精度,使用可变化的局部边缘模式（Varied Local Edge Pattern,VLEP）算法,利用像元及其近邻的灰度变化进行区域统计,同时从多尺度和多方向的角度提取纹理边缘特征。然而,当图像分辨率发生变化,或图像受到光照、反射的影响时,纹理计算可能会出现较大偏差。为此,在VLEP算法的基础上,提出主导学习框架相似纹理分类方法,通过构建全局主导模式集,解决纹理计算偏差导致的类间距离小和类内距离大的问题。实验结果表明,主导边缘模式思想可以有效地提高相似纹理图像的分类准确率。     

Salient feature and reliable classifier selection for facial expression classification

A novel facial expression classification (FEC) method is presented and evaluated. The classification process is decomposed into multiple two-class classification problems, a choice that is analytically justified, and unique sets of features are extracted for each classification problem. Specifically, for each two-class problem, an iterative feature selection process that utilizes a class separability measure is employed to create salient feature vectors (SFVs), where each SFV is composed of a selected feature subset. Subsequently, two-class discriminant analysis is applied on the SFVs to produce salient discriminant hyper-planes (SDHs), which are used to train the corresponding two-class classifiers. To properly integrate the two-class classification results and produce the FEC decision, a computationally efficient and fast classification scheme is developed. During each step of this scheme, the most reliable classifier is identified and utilized, thus, a more accurate final classification decision is produced. The JAFFE and the MMI databases are used to evaluate the performance of the proposed salient-feature-and-reliable-classifier selection (SFRCS) methodology. Classification rates of 96.71% and 93.61% are achieved under the leave-one-sample-out evaluation strategy, and 85.92% under the leave-one-subject-out evaluation strategy.     

Class-specific feature polynomial classifier for pattern classification and its application to handwritten numeral recognition

The polynomial classifier (PC) that takes the binomial terms of reduced subspace features as inputs has shown superior performance to multilayer neural networks in pattern classification. In this paper, we propose a class-specific feature polynomial classifier (CFPC) that extracts class-specific features from class-specific subspaces, unlike the ordinary PC that uses a class-independent subspace. The CFPC can be viewed as a hybrid of ordinary PC and projection distance method. The class-specific features better separate one class from the others, and the incorporation of class-specific projection distance further improves the separability. The connecting weights of CFPC are efficiently learned class-by-class to minimize the mean square error on training samples. To justify the promise of CFPC, we have conducted experiments of handwritten digit recognition and numeral string recognition on the NIST Special Database 19 (SD19). The digit recognition task was also benchmarked on two standard databases USPS and MNIST. The results show that the performance of CFPC is superior to that of ordinary PC, and is competitive with support vector classifiers (SVCs).     

Channel and feature selection for a surface electromyographic pattern recognition task

The objective of this research is to select a reduced group of surface electromyographic (sEMG) channels and signal-features that is able to provide an accurate classification rate in a myoelectric control system for any user. To that end, the location of 32 sEMG electrodes placed around-along the forearm and 86 signal-features are evaluated simultaneously in a static-hand gesture classification task (14 different gestures). A novel multivariate variable selection filter method named mRMR-FCO is presented as part of the selection process. This process finds the most informative and least redundant combination of sEMG channels and signal-features among all the possible ones. The performance of the selected set of channels and signal-features is evaluated with a Support Vector Machine classifier.     

A survey of document image classification: problem statement,classifier architecture and performance evaluation

Document image classification is an important step in Office Automation, Digital Libraries, and other document image analysis applications. There is great diversity in document image classifiers: they differ in the problems they solve, in the use of training data to construct class models, and in the choice of document features and classification algorithms. We survey this diverse literature using three components: the problem statement, the classifier architecture, and performance evaluation. This brings to light important issues in designing a document classifier, including the definition of document classes, the choice of document features and feature representation, and the choice of classification algorithm and learning mechanism. We emphasize techniques that classify single-page typeset document images without using OCR results. Developing a general, adaptable, high-performance classifier is challenging due to the great variety of documents, the diverse criteria used to define document classes, and the ambiguity that arises due to ill-defined or fuzzy document classes.     

A support vector machine classifier with automatic confidence and its application to gender classification

In this paper, we propose a support vector machine with automatic confidence (SVMAC) for pattern classification. The main contributions of this work to learning machines are twofold. One is that we develop an algorithm for calculating the label confidence value of each training sample. Thus, the label confidence values of all of the training samples can be considered in training support vector machines. The other one is that we propose a method for incorporating the label confidence value of each training sample into learning and derive the corresponding quadratic programming problems. To demonstrate the effectiveness of the proposed SVMACs, a series of experiments are performed on three benchmarking pattern classification problems and a challenging gender classification problem. Experimental results show that the generalization performance of our SVMACs is superior to that of traditional SVMs.     

Analytic network process for pattern classification problems using genetic algorithms

The analytic network process (ANP) is a useful technique for multi-attribute decision analysis (MCDA) that employs a network representation to describe interrelationships between diverse attributes. Owing to effectiveness of the ANP in allowing for complex interrelationships between attributes, this paper develops an ANP-based classifier for pattern classification problems with interdependence or independence among attributes. To deal with interdependence, this study employs genetic algorithms (GAs) to automatically determine elements in the supermatrix that are not easily user-specified, to find degrees of importance of respective attributes. Then, with the relative importance for each attribute in the limiting supermatrix, the current work determines the class label of a pattern by its synthetic evaluation. Experimental results obtained by the proposed ANP-based classifier are comparable to those obtained by other fuzzy or non-fuzzy classification methods.     

Pattern classification using a linear associative memory

Pattern classification is a very important image processing task. A typical pattern classification algorithm can be broken into two parts; first, the pattern features are extracted and, second, these features are compared with a stored set of reference features until a match is found. In the second part, usually one of the several clustering algorithms or similarity measures is applied. In this paper, a new application of linear associative memory (LAM) to pattern classification problems is introduced. Here, the clustering algorithms or similarity measures are replaced by a LAM matrix multiplication. With a LAM, the reference features need not be separately stored. Since the second part of most classification algorithms is similar, a LAM standardizes the many clustering algorithms and also allows for a standard digital hardware implementation. Computer simulations on regular textures using a feature extraction algorithm achieved a high percentage of successful classification. In addition, this classification is independent of topological transformations.     

One-against-all ensemble for multiclass pattern classification

This paper presents a new method called one-against-all ensemble for solving multiclass pattern classification problems. The proposed method incorporates a neural network ensemble into the one-against-all method to improve the generalization performance of the classifier. The experimental results show that the proposed method can reduce the uncertainty of the decision and it is comparable to the other widely used methods.     

Fuzzy quartile encoding as a preprocessing method for biomedical pattern classification

A fuzzy set based preprocessing method is described that may be used in the classification of patterns. This method, dispersion-adjusted fuzzy quartile encoding, determines the respective degrees to which a feature (attribute) belongs to a collection of fuzzy sets that overlap at the respective quartile boundaries of the feature. The fuzzy sets are adjusted to take into account the overall dispersion of values for a feature. The membership values are subsequently used in place of the original feature value. This transformation has a normalizing effect on the feature space and is robust to feature outliers. This preprocessing method, empirically evaluated using five biomedical datasets, is shown to improve the discriminatory power of the underlying classifiers.     

Locally nearest neighbor classifiers for pattern classification

In this paper, two novel classifiers based on locally nearest neighborhood rule, called nearest neighbor line and nearest neighbor plane, are presented for pattern classification. Comparison to nearest feature line and nearest feature plane, the proposed methods take much lower computation cost and achieve competitive performance.